scholarly journals Remaining fatigue life assessment of in-service road bridge decks based upon artificial neural networks

2018 ◽  
Vol 171 ◽  
pp. 602-616 ◽  
Author(s):  
Eissa Fathalla ◽  
Yasushi Tanaka ◽  
Koichi Maekawa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fatigue Life ◽  
Bridge Decks ◽  
Life Assessment ◽  
Fatigue Life Assessment ◽  
Artificial Neural ◽  
Remaining Fatigue Life

scholarly journals Prediction of aeroelastic response of bridge decks using artificial neural networks

2020 ◽  
Vol 231 ◽  
pp. 106198 ◽  
Author(s):  
Tajammal Abbas ◽  
Igor Kavrakov ◽  
Guido Morgenthal ◽  
Tom Lahmer
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bridge Decks ◽  
Aeroelastic Response ◽  
Artificial Neural

Fatigue Life Assessment for NPS30 Steel Pipe

2012 ◽  
Author(s):  
Jorge Silva ◽  
Hossein Ghaednia ◽  
Sreekanta Das
Keyword(s):  
Fatigue Life ◽  
Test Data ◽  
Crack Depth ◽  
Research Work ◽  
Longitudinal Crack ◽  
Assessment Model ◽  
Life Assessment ◽  
Fatigue Life Assessment ◽  
Remaining Fatigue Life ◽  
Crack Defect

Pipeline is the common mode for transporting oil, gas, and various petroleum products. Aging and corrosive environment may lead to formation of various defects such as crack, dent, gouge, and corrosion. The performance evaluation of field pipelines with crack defect is important. Accurate assessment of crack depth and remaining fatigue life of pipelines with crack defect is vital for pipeline’s structural integrity, inspection interval, management, and maintenance. An experimental based research work was completed at the University of Windsor for developing a semi-empirical model for estimating the remaining fatigue life of oil and gas pipes when a longitudinal crack defect has formed. A statistical approach in conjunction with fracture mechanics was used to develop this model. Statistical analysis was undertaken on CT specimen data to develop this fatigue life assessment model. Finite element method was used for determining the stress intensity factor. The fatigue life assessment model was then validated using full-scale fatigue test data obtained from 762 mm (30 inch) diameter X65 pipe. This paper discusses the test specimens and test data obtained from this study. Development and validation of the fatigue life assessment model is also presented in this paper.

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